DETECTION OF METAL-INDUCED VEGETATION ANOMALIES IN THE MINING AREA USING REMOTE SENSING INDICES

A lot of information can be obtained about vegetation with remote sensing technologies and multispectral satellite data. This study used Landsat-7 ETM+ (Enhanced Thematic Mapper) satellite data and remote sensing indices to identify stress-related anomalies in the vegetation at the mining area. Four different remote sensing indices were used: Vegetation Index considering Greenness and Shortwave infrared (VIGS), Normalized Pigment Chlorophyll Ratio Index (NPCRI), Enhanced Vegetation Index (EVI) and Global Environmental Monitoring Index (GEMI). Box plot, independent sample t-test and mean/standard deviation value methods were used to compare the values taken from the sample points at mining area and non-mining area. The box plots of the indices show that the median VIGS values at mining area were higher than those non-mining area in both July and October. The differences between the median values were lowest at the EVI index. Independent samples t-test results revealed significant differences at p < 0.05 between mining area and non-mining area. No anomalies were detected in the VIGS and NPCRI index results when comparing mean/ standard deviation values. The lower mean/ standard deviation values of the EVI and GEMI indices during October indicate an anomaly caused by stress. The findings demonstrated that multiple indices, temporal and statistical analysis approaches can be used to determine vegetation stress in mining areas.